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題名 聯準會量化寬鬆政策對美國股債動態條件相關性之影響
The impact of the Federal Reserve Quantitative Easing on Dynamic Conditional Correlations between US stock and bond
作者 李昀
Lee, Yun
貢獻者 陳聖賢
李昀
Lee, Yun
關鍵詞 股債報酬率相關性
量化寬鬆
貨幣供給量
動態條件相關係數模型
Stock-bond correlations
Quantitative easing
Money supply
DCC-GARCH
日期 2021
上傳時間 3-Jul-2021 00:39:03 (UTC+8)
摘要 股票及債券為投資組合資產配置中兩大常見且重要的金融資產，美國為全球主要金融市場之一，了解美國股債相關性變化並探討影響股債相關性變化之因素，將有助於投資人進行投資組合之資產配置及風險控管。受到2008年金融海嘯影響，美國聯準會於2008年至2014年多次實施量化寬鬆政策來穩定市場流動性並刺激經濟； 2020年為因應Covid–19的衝擊，更進一步實施無限量量化寬鬆政策；我們可以觀察到近年聯準會更加頻繁的透過量化寬鬆政策來穩定金融市場，使市場資金量持續增長。故本研究將探討美國聯準會執行量化寬鬆政策和市場貨幣供給量增長對於股債相關性之影響。

本研究分為兩個部份，第一部分是利用Engle（2002）動態條件相關係數（DCC-GARCH）模型來探討自2001年至2021年美國股票及債券相關性之變化。研究發現美國股債動態相關係數持續隨時間改變，主要介於-0.3至-0.7之中度負相關，平均動態相關係數達-0.45，顯示適當的配置股票和債券於投資組合中將能有效發揮分散風險的功能。

第二部分為採用OLS迴歸分析探討2003年至2021年聯準會量化寬鬆政策及市場貨幣供給量對於美國股債相關性之影響。實證結果顯示聯準會之量化寬鬆政策對於股債相關性有顯著負向影響，說明當聯準會執行量化寬鬆政策導致所持有的公債、Agency Debt和Agency MBS增加，股債相關係數會降低（負相關增加），此一現象在QE1和QE2的時候較為明顯；貨幣供給量和股債相關性呈現顯著負相關，貨幣供給量增加可能使市場對未來經濟表現保持樂觀態度、降低要求之風險溢酬，使股價上升、債券價格下滑，股債相關係數下降。
Stock and bond are two main asset classes in investors’ portfolios. The United State is an important financial market in the world. Therefore, investigating the changes and determinants of the US stock-bond correlations is critical for investors to allocate their assets and control risks. Due to the financial crisis in 2008, the Federal Reserve implemented a series of quantitative easing policy to restore market liquidity and stimulate the economy from 2008 to 2014. In 2020, the Fed announced an unlimited QE to support the financial market affected by the coronavirus pandemic. We can find that the Fed uses QE policy to stabilize the financial market more frequently in recent years, leading to the growth of money supply. This study will discuss impacts of the QE and money supply on the US stock-bond correlations.

The study is divided into two parts. First of all, I use DCC-GARCH model (Engle, 2002) to build the US dynamic conditional stock-bond correlations from 2001 to 2021. The empirical results show that the US stock-bond correlation coefficient changes over time with an average of -0.45, which means that allocating stock and bond appropriately will effectively diversify the portfolio and minimize the risks. Next, I use OLS regression to investigate the impact of the QE policy and money supply on the US stock-bond correlations. The empirical results show that the QE policy and money supply are important determinants of the US stock-bond correlations. The QE policy has a significantly negative relationship with the US stock-bond correlations, especially during QE1 and QE2. Money supply also has a significantly negative relationship with the US stock-bond correlations.
參考文獻 1. 中央銀行（2013）。量化寬鬆貨幣政策。中央銀行理監事會後編印報告，1-33。
2. 朱美智（2016）。Fed及ECB因應危機措施對其資產負債表之影響。國際金融參考資料，69，42-69。
3. 張志揚（2012）。美國非傳統貨幣政策之採行及其影響。國際金融參考資料，63，23-46。
4. 陳旭昇（2013）。時間序列分析--總體經濟與財務金融之應用。台灣：東華。
5. 廖四郎、林建秀（2018）。美國歷次QE對亞洲各國股匯市波動性研究。財團法人台北外匯市場發展基金會專題研究計畫。
6. Andersson, M., Krylova, E., and Vähämaa, S. (2008). Why does the correlation between stock and bond returns vary over time? Applied Financial Economics 18(2), 139-151.
7. Aslandis, Q., and Christainsen, C. (2014). Quantiles of the realized stock-bond correlation and links to the macroeconomy. Journal of Empirical Finance 28, 321-331.
8. Asgharian, H., Christainsen, C., and Hou, A. J. (2016). Macro-finance determinants of the long run stock bond correlation: the DCC-MIDAS specification. Journal of Finance Econometrics 14(3), 617-642.
9. Bhattarai, S., Chatterjee, A., and Park W. Y. (2015). Effects of US quantitative easing on emerging market economies. Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute Working Paper No. 255.
10. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3), 307-327.
11. Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Review of Economics and Statistics 72(3), 498-505.
12. Bollerslev, T., Chou, R. Y., and Kroner, K. F. (1992). ARCH modeling in finance: a review of the theory and empirical evidence. Journal of Econometrics 52(1-2), 5–59.
13. Baele, L., Bekaert, G., and Inghelbrecht, K. (2010). The determinants of stock and bond return comovements. Review of Financial Studies 23(6), 2374–2428.
14. Cenedese G., and Elard L. (2021). Unconventional monetary policy and the portfolio choice of international mutual funds. Journal of International Money and Finance, forthcoming.
15. Chaudhuri, K., and Smiles, S. (2004). Stock market and aggregate economic activity: evidence from Australia. Applied Financial Economics 14(2), 121–129.
16. Connolly R., Stivers, C., and Sun, L. (2005). Stock market uncertainty and the stock-bond return relation. Journal of Financial and Quantitative Analysis 40(1), 161-194.
17. Dimic, N., Kiviaho, J., Piljak, V., and Äijö, J. (2016). Impact of financial market uncertainty and macroeconomic factors on stock–bond correlation in emerging markets. Research in International Business and Finance 36, 41–51.
18. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4), 987-1007.
19. Engle, R. (2002). Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics 20(3), 339-350.
20. Fang, L., Yu, H., and Li, L. (2017). The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets. Economic Modelling 66, 139–145.
21. Fang, L., Yu, H., and Huang, Y. (2018). The role of investor sentiment in the long-term correlation between U.S. stock and bond markets. International Review of Economics & Finance 58, 127-139.
22. Gokmenoglu, K. K., and Hadood, A. A. A. (2020). Impact of US unconventional monetary policy on dynamic stock-bond correlations: portfolio rebalancing and signalling channel effects. Finance Research Letters, forthcoming.
23. Goldberg, L., and Leonard, D. (2005). What moves sovereign bond markets? The effects of economic news on U.S. and German yields. Current Issues in Economics and Finance 9(9).
24. Humpe, A., and McMillan, D. (2020). The Covid-19 stock market puzzle and money supply in the US. Economics Bulletin 40(4), 3104-3110.
25. Kim, S. J., Moshirian, F., and Wu, E. (2005). Evolution of international stock and bond market integration: influence of the European monetary union. Journal of Banking & Finance 30, 1507-1534.
26. Kryzanowski, L., Zhang, J., and Zhong, R. (2017). Cross-financial-market correlations and quantitative easing. Finance Research Letters 20, 13–21.
27. McMillan, D. G. (2017). Does money supply growth contain predictive power for stock returns? evidence and explanation. International Journal of Banking, Accounting and Finance 8(2), 119-145.
28. Pícha, V. (2017). Effect of money supply on the stock market. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, 465-472.
29. Ruman, A. M. (2021). Stock market implications of Fed`s balance sheet size. Journal of Economic Studies, forthcoming.
30. Skintzi, V. D. (2019). Determinants of stock-bond market comovement in the Eurozone under model uncertainty. International Review of Financial Analysis 61, 20–28.
31. Williams, J. C. (2011). Unconventional monetary policy: lessons from the past three years. Paper presented at the Swiss National Bank Research Conference, Zurich, Switzerland.
32. Yang, J., Zhou, Y., and Wang, Z. (2009). The stock–bond correlation and macroeconomic conditions: one and a half centuries of evidence. Journal of Banking & Finance 33, 670-680.
描述 碩士
國立政治大學
財務管理學系
108357006
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108357006
資料類型 thesis
dc.contributor.advisor 陳聖賢zh_TW
dc.contributor.author (Authors) 李昀zh_TW
dc.contributor.author (Authors) Lee, Yunen_US
dc.creator (作者) 李昀zh_TW
dc.creator (作者) Lee, Yunen_US
dc.date (日期) 2021en_US
dc.date.accessioned 3-Jul-2021 00:39:03 (UTC+8)-
dc.date.available 3-Jul-2021 00:39:03 (UTC+8)-
dc.date.issued (上傳時間) 3-Jul-2021 00:39:03 (UTC+8)-
dc.identifier (Other Identifiers) G0108357006en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136050-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財務管理學系zh_TW
dc.description (描述) 108357006zh_TW
dc.description.abstract (摘要) 股票及債券為投資組合資產配置中兩大常見且重要的金融資產，美國為全球主要金融市場之一，了解美國股債相關性變化並探討影響股債相關性變化之因素，將有助於投資人進行投資組合之資產配置及風險控管。受到2008年金融海嘯影響，美國聯準會於2008年至2014年多次實施量化寬鬆政策來穩定市場流動性並刺激經濟； 2020年為因應Covid–19的衝擊，更進一步實施無限量量化寬鬆政策；我們可以觀察到近年聯準會更加頻繁的透過量化寬鬆政策來穩定金融市場，使市場資金量持續增長。故本研究將探討美國聯準會執行量化寬鬆政策和市場貨幣供給量增長對於股債相關性之影響。

本研究分為兩個部份，第一部分是利用Engle（2002）動態條件相關係數（DCC-GARCH）模型來探討自2001年至2021年美國股票及債券相關性之變化。研究發現美國股債動態相關係數持續隨時間改變，主要介於-0.3至-0.7之中度負相關，平均動態相關係數達-0.45，顯示適當的配置股票和債券於投資組合中將能有效發揮分散風險的功能。

第二部分為採用OLS迴歸分析探討2003年至2021年聯準會量化寬鬆政策及市場貨幣供給量對於美國股債相關性之影響。實證結果顯示聯準會之量化寬鬆政策對於股債相關性有顯著負向影響，說明當聯準會執行量化寬鬆政策導致所持有的公債、Agency Debt和Agency MBS增加，股債相關係數會降低（負相關增加），此一現象在QE1和QE2的時候較為明顯；貨幣供給量和股債相關性呈現顯著負相關，貨幣供給量增加可能使市場對未來經濟表現保持樂觀態度、降低要求之風險溢酬，使股價上升、債券價格下滑，股債相關係數下降。		zh_TW
dc.description.abstract (摘要) Stock and bond are two main asset classes in investors’ portfolios. The United State is an important financial market in the world. Therefore, investigating the changes and determinants of the US stock-bond correlations is critical for investors to allocate their assets and control risks. Due to the financial crisis in 2008, the Federal Reserve implemented a series of quantitative easing policy to restore market liquidity and stimulate the economy from 2008 to 2014. In 2020, the Fed announced an unlimited QE to support the financial market affected by the coronavirus pandemic. We can find that the Fed uses QE policy to stabilize the financial market more frequently in recent years, leading to the growth of money supply. This study will discuss impacts of the QE and money supply on the US stock-bond correlations.

The study is divided into two parts. First of all, I use DCC-GARCH model (Engle, 2002) to build the US dynamic conditional stock-bond correlations from 2001 to 2021. The empirical results show that the US stock-bond correlation coefficient changes over time with an average of -0.45, which means that allocating stock and bond appropriately will effectively diversify the portfolio and minimize the risks. Next, I use OLS regression to investigate the impact of the QE policy and money supply on the US stock-bond correlations. The empirical results show that the QE policy and money supply are important determinants of the US stock-bond correlations. The QE policy has a significantly negative relationship with the US stock-bond correlations, especially during QE1 and QE2. Money supply also has a significantly negative relationship with the US stock-bond correlations.		en_US
dc.description.tableofcontents 第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 7
第三節 研究流程與架構 9
第貳章 文獻回顧 10
第參章 資料處理及變數設定 13
第一節 應變數資料來源及處理 13
第二節 解釋變數資料來源及處理 13
第三節 研究假說 18
第肆章 研究方法 20
第一節 單根檢定 20
第二節 落後期數選擇 21
第三節 動態條件相關係數（DCC-GARCH）模型 21
第四節 OLS迴歸分析 27
第伍章 實證結果及分析 29
第一節 資料敘述統計及初步分析 29
第二節 單根檢定 36
第三節 動態條件相關係數模型設定 37
第四節 OLS迴歸分析 40
第陸章 結論與未來研究建議 43
第一節 結論 43
第二節 研究限制及未來研究建議 44
參考文獻 45		zh_TW
dc.format.extent 2279142 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108357006en_US
dc.subject (關鍵詞) 股債報酬率相關性zh_TW
dc.subject (關鍵詞) 量化寬鬆zh_TW
dc.subject (關鍵詞) 貨幣供給量zh_TW
dc.subject (關鍵詞) 動態條件相關係數模型zh_TW
dc.subject (關鍵詞) Stock-bond correlationsen_US
dc.subject (關鍵詞) Quantitative easingen_US
dc.subject (關鍵詞) Money supplyen_US
dc.subject (關鍵詞) DCC-GARCHen_US
dc.title (題名) 聯準會量化寬鬆政策對美國股債動態條件相關性之影響zh_TW
dc.title (題名) The impact of the Federal Reserve Quantitative Easing on Dynamic Conditional Correlations between US stock and bonden_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. 中央銀行（2013）。量化寬鬆貨幣政策。中央銀行理監事會後編印報告，1-33。
2. 朱美智（2016）。Fed及ECB因應危機措施對其資產負債表之影響。國際金融參考資料，69，42-69。
3. 張志揚（2012）。美國非傳統貨幣政策之採行及其影響。國際金融參考資料，63，23-46。
4. 陳旭昇（2013）。時間序列分析--總體經濟與財務金融之應用。台灣：東華。
5. 廖四郎、林建秀（2018）。美國歷次QE對亞洲各國股匯市波動性研究。財團法人台北外匯市場發展基金會專題研究計畫。
6. Andersson, M., Krylova, E., and Vähämaa, S. (2008). Why does the correlation between stock and bond returns vary over time? Applied Financial Economics 18(2), 139-151.
7. Aslandis, Q., and Christainsen, C. (2014). Quantiles of the realized stock-bond correlation and links to the macroeconomy. Journal of Empirical Finance 28, 321-331.
8. Asgharian, H., Christainsen, C., and Hou, A. J. (2016). Macro-finance determinants of the long run stock bond correlation: the DCC-MIDAS specification. Journal of Finance Econometrics 14(3), 617-642.
9. Bhattarai, S., Chatterjee, A., and Park W. Y. (2015). Effects of US quantitative easing on emerging market economies. Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute Working Paper No. 255.
10. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3), 307-327.
11. Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Review of Economics and Statistics 72(3), 498-505.
12. Bollerslev, T., Chou, R. Y., and Kroner, K. F. (1992). ARCH modeling in finance: a review of the theory and empirical evidence. Journal of Econometrics 52(1-2), 5–59.
13. Baele, L., Bekaert, G., and Inghelbrecht, K. (2010). The determinants of stock and bond return comovements. Review of Financial Studies 23(6), 2374–2428.
14. Cenedese G., and Elard L. (2021). Unconventional monetary policy and the portfolio choice of international mutual funds. Journal of International Money and Finance, forthcoming.
15. Chaudhuri, K., and Smiles, S. (2004). Stock market and aggregate economic activity: evidence from Australia. Applied Financial Economics 14(2), 121–129.
16. Connolly R., Stivers, C., and Sun, L. (2005). Stock market uncertainty and the stock-bond return relation. Journal of Financial and Quantitative Analysis 40(1), 161-194.
17. Dimic, N., Kiviaho, J., Piljak, V., and Äijö, J. (2016). Impact of financial market uncertainty and macroeconomic factors on stock–bond correlation in emerging markets. Research in International Business and Finance 36, 41–51.
18. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4), 987-1007.
19. Engle, R. (2002). Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics 20(3), 339-350.
20. Fang, L., Yu, H., and Li, L. (2017). The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets. Economic Modelling 66, 139–145.
21. Fang, L., Yu, H., and Huang, Y. (2018). The role of investor sentiment in the long-term correlation between U.S. stock and bond markets. International Review of Economics & Finance 58, 127-139.
22. Gokmenoglu, K. K., and Hadood, A. A. A. (2020). Impact of US unconventional monetary policy on dynamic stock-bond correlations: portfolio rebalancing and signalling channel effects. Finance Research Letters, forthcoming.
23. Goldberg, L., and Leonard, D. (2005). What moves sovereign bond markets? The effects of economic news on U.S. and German yields. Current Issues in Economics and Finance 9(9).
24. Humpe, A., and McMillan, D. (2020). The Covid-19 stock market puzzle and money supply in the US. Economics Bulletin 40(4), 3104-3110.
25. Kim, S. J., Moshirian, F., and Wu, E. (2005). Evolution of international stock and bond market integration: influence of the European monetary union. Journal of Banking & Finance 30, 1507-1534.
26. Kryzanowski, L., Zhang, J., and Zhong, R. (2017). Cross-financial-market correlations and quantitative easing. Finance Research Letters 20, 13–21.
27. McMillan, D. G. (2017). Does money supply growth contain predictive power for stock returns? evidence and explanation. International Journal of Banking, Accounting and Finance 8(2), 119-145.
28. Pícha, V. (2017). Effect of money supply on the stock market. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, 465-472.
29. Ruman, A. M. (2021). Stock market implications of Fed`s balance sheet size. Journal of Economic Studies, forthcoming.
30. Skintzi, V. D. (2019). Determinants of stock-bond market comovement in the Eurozone under model uncertainty. International Review of Financial Analysis 61, 20–28.
31. Williams, J. C. (2011). Unconventional monetary policy: lessons from the past three years. Paper presented at the Swiss National Bank Research Conference, Zurich, Switzerland.
32. Yang, J., Zhou, Y., and Wang, Z. (2009). The stock–bond correlation and macroeconomic conditions: one and a half centuries of evidence. Journal of Banking & Finance 33, 670-680.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202100565en_US